Security
Defensive architectures to prevent data leakage, intercept adversarial payloads, and establish agent execution guardrails.
Contextual Boundary
Why This Exists
Most AI discussions focus on model capabilities. My work focuses on what happens after deployment. As AI systems become embedded in products, organizations face a new class of problems involving economics, governance, security, reliability, and operational control. The Production AI Governance Framework exists to help organizations understand, measure, and manage those challenges.
Core Analytical Axioms
Forensically proven concepts in this operational boundary.
AI Agent Kill Switch
A deterministic runtime boundary that intercepts and terminates autonomous agent loops before they generate legal or financial liability.
AI agents are given tools (database access, API keys) without absolute boundaries, leading to recursive feedback loops that consume budget or delete data.
Prevents rogue agents from causing catastrophic operational crashes.
Adversarial Injection Shield
State-verification and schema-enforcement gates that isolate LLM prompt variables.
Attackers inject system-override prompts into input forms, bypassing guardrails and capturing database context.
Protects proprietary system instructions and blocks data exfiltration.
Shadow AI Scanner
Forensic evaluation to detect employee data exposure to unauthorized external models.
Employees copy-paste proprietary code, customer records, or financial spreadsheets into public LLM interfaces, breaching compliance.
Prevents intellectual property loss and guarantees compliance with SOC2 and GDPR.
Want to apply this to your organization?
Run a free diagnostic first. If the numbers concern you, book a session to build a remediation plan.
Richard Ewing — AI Economist & Capital Auditor